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Automatic abstractive summaries are found to often distort or fabricate facts in the article.
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Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D Manning. 2015 · 2015
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Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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A neural attention model for abstractive sentence summarization
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
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Taku Kudo and John Richardson. 2018 · 2018
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Ensure the correctness of the summary: Incorporate entailment knowledge into abstractive sentence summarization
Haoran Li, Junnan Zhu, Jiajun Zhang, and Chengqing Zong. 2018 · 2018
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
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Faithful to the original: Fact aware neural abstractive summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander M Rush. 2018 · 2018
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Tobias Falke, Leonardo FR Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019 · 2019
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Assessing the factual accuracy of generated text
Ben Goodrich, Vinay Rao, Peter J Liu, and Mohammad Saleh. 2019 · 2019
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Mind the facts: Knowledge-boosted coherent abstractive text summarization
Beliz Gunel, Chenguang Zhu, Michael Zeng, and Xuedong Huang. 2019 · 2019
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Neural text summarization: A critical evaluation
Wojciech Kryściński, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019a · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
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Factual error correction for abstractive summarization models
Meng Cao, Yue Dong, Jiapeng Wu, and Jackie Chi Kit Cheung. 2020 · 2020
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Multi-fact correction in abstractive text summarization
Yue Dong, Shuohang Wang, Zhe Gan, Yu Cheng, Jackie Chi Kit Cheung, and Jingjing Liu. 2020 · 2020
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2020
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